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Ramis M. - Fullstack Developer, OpenAI API, n8n

Being part of Softaims has allowed me to see the full spectrum of what technology can achieve when guided by empathy, discipline, and creativity. Each assignment, regardless of size, represents an opportunity to bring clarity to complexity and to turn ambitious ideas into tangible outcomes. I’ve come to realize that successful development isn’t just about writing code—it’s about listening carefully, understanding deeply, and designing thoughtfully. Every client brings unique challenges, and I make it a priority to align my work with their goals, ensuring that the end result is both effective and lasting. Softaims fosters an environment where collaboration is not optional—it’s essential. The collective expertise within the team pushes me to think beyond conventional boundaries, to question, refine, and innovate. I believe that this process of shared learning and experimentation is what makes our solutions resilient and impactful. My ultimate goal is to build technology that feels effortless to use yet powerful in function. I approach every task with the mindset that small details can make a big difference. Through continuous refinement and dedication, I aim to contribute to the kind of work that not only serves today’s needs but anticipates tomorrow’s possibilities.

Main technologies

  • Fullstack Developer

    3 years

  • Python

    1 Year

  • Machine Learning

    1 Year

  • Back-End Development

    1 Year

Additional skills

  • Python
  • Machine Learning
  • Back-End Development
  • CI/CD
  • Google Cloud Platform
  • Amazon Web Services
  • Artificial Intelligence
  • Large Language Model
  • Multimodal Large Language Model
  • Natural Language Processing
  • Azure
  • Computer Vision
  • YOLO
  • OpenAI API
  • n8n

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Experience Highlights

Multi-AI Agents

Architected and deployed the World’s first grid search infrastructure multi-agent RAG systems, integrated tool-calling workflows and optimized p95 latency under 500ms across diverse LLMs using LangGraph/LangChain using AWS Bedrock KnowledgeBase.

Arthur

Developed a fine-tuned DeepSeek-R1-Distill-Llama-70B using QLoRA with Multi-GPU training on 8xH100 with FSDP, targeting reasoning in STEM and electrical engineering. Designed a QA pipeline with PyMuPDF parsing, Qwen2.5-VL OCR, DeepSeek-r1 prompts for multi-angled questions, Llama-405B judge, and retrieval via ChromaDB+text-embedding-3-large with multi-stage checkpoints. Curated 50k+ synthetic datasets (MATH, Physics, Petroleum, etc.) trained 6 epochs. Benchmarked Arthur on ElecBench & STEM-AI-mtl QA, achieving 87% accuracy, 0.61 recall, 0.46 F1, with BERT score + LLM-as-judge.

Jeff Booth- AI Mentor

Integrated AssemblyAI to transcribe and label Jeff Booth’s video content. Built a LangChain-ChromaDB pipeline to embed transcripts for contextual retrieval. Used GPT-4 Turbo with persona-tuned prompts to create a chatbot that mimics Jeff’s tone and logic. Implemented a Dropbox watcher for auto-updating the vector store. Stored session-based user memory in PostgreSQL. Deployed the chatbot as a FastAPI backend API for seamless integration. Deployed it on AWS EC2, and AWS Serverless to maintain scalability and smooth experience.

Complain Partner

Developed a FastAPI application using Python, featuring an agent built with LangGraph and powered by GPT-4o-mini as the base LLM. The application accepts PDFs, images, and text for registering complaints. Preprocessed PDFs using the Unstructured tool with OCR for better data extraction. The GPT agent utilizes custom tools and functions developed with LangChain for decision-making, interacts with third-party APIs, and determines which API endpoints to invoke based on user input. The agent serves as a complaint registrar, ensuring all prerequisites are met

ChatGPT/GPT-4 Chatbots

Objective: Develop a Python application leveraging the langchain library. This application should be capable of engaging in conversations with users, storing crucial information in a vector database, and utilizing historical and vector data to respond to inquiries. The Awesome ChatBot Project is a conversational AI assistant with both backend and frontend components. The backend uses Python, Flask, ChromaDB, Langchain, and OpenAI to enable advanced natural language processing. The frontend provides an interactive user interface built with React.JS. To set up the project, Python 3.10+ and Node.js need to be installed. The backend requires creating a virtual environment, installing dependencies like Visual Studio C++ Build Tools, and running app.py. The frontend needs installing npm packages and running npm start. Once running, the frontend seamlessly connects to the backend to deliver a robust chatbot experience. Users can have natural conversations with the AI assistant named Claude. The project utilizes cutting-edge NLP techniques to understand user input and provide relevant responses. Overall, this project enables developers to build a production-ready chatbot leveraging Python and React.JS. The README provides clear instructions to run both frontend and backend components locally. Contributors are welcome to help improve the chatbot's capabilities.

Education

  • FAST National University of Computer and Emerging Sciences

    Bachelor's degree in Computer science

Languages

  • English

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